In an electromagnetic context, the sensors are antennas and the radiofrequency sources are propagated according to a polarization that is dependent on the transmitting antenna. In an acoustic context, the sensors are microphones and the sources are sounds.
FIG. 1 shows that an antenna processing system comprises an array 102 of sensors receiving sources with different angles of arrival θmp. The individual sensors 101 of the array receive the sources E1, E2 with a phase and an amplitude that are dependent in particular on their angles of incidence and on the position of the sensors. The angles of incidence are parameterized in one dimension (1D) by the azimuth θm and in two dimensions (2D) by the azimuth θm and the elevation Δm.
According to FIG. 2, a 1D goniometry is defined by techniques which estimate only the azimuth by assuming that the waves of the sources are propagated in the plane 201 of the array of sensors. When the goniometry technique jointly estimates the azimuth and elevation of a source, it is an issue of 2D goniometry.
The aim of the antenna processing techniques is notably to exploit the spatial diversity generated by the multiple-antenna reception of the incidence signals, in other words, to use the position of the antennas of the array to better use the differences in incidence and distance of the sources.
FIG. 3 illustrates an application to goniometry in the presence of multiple paths. The m-th source 301 is propagated along P paths 311, 312, 313 of incidences θmp (1≦p≦P) which are provoked by P−1 obstacles 320 in the radiofrequency environment. The problem dealt with in the method according to the invention is, notably, to perform a 2D goniometry for coherent paths in which the propagation time deviation between the direct path and a secondary path is very low.
One known method for doing the goniometry is the MUSIC algorithm [1]. However, this algorithm does not make it possible to estimate the incidences of the sources in the presence of coherent paths.
The algorithms that make it possible to process the case of coherent sources are the maximum likelihood algorithms [2][3] which are applicable to arrays of sensors with any geometry. However, these techniques require the calculation of a multidimensional criteria of which the number of dimensions depends on the number of paths and on the number of incidence parameters for each path. More particularly, in the presence of K paths, the criterion has 2K dimensions for a 2D goniometry in order to jointly estimate all the incidences (Θ1, . . . , ΘK). It should be noted that, even in the presence of a number K′ of coherent paths that is less than the total number K of paths, the calculation of the maximum likelihood criterion still has 2K dimensions. In order to reduce the number of dimensions of the criterion to 2K′, one alternative is to apply the coherent MUSIC method [4]. However, the coherent MUSIC algorithm [4] requires a high number of sensors and very significant computation resources.
Another alternative for reducing the computation cost is to implement spatial smoothing or forward-backward techniques [5][6], these techniques requiring particular array geometries. In practice, spatial smoothing is applicable when the array is broken down into subarrays having the same geometry (e.g.: evenly-spaced linear array or array on a regular 2D grid). The forward-backward algorithm requires an array with a center of symmetry. These techniques are highly restrictive in terms of geometry of the array of sensors, especially for a 2D goniometry, in which the constraint of symmetry or of translated identical subarrays is difficult to satisfy.
For 1D goniometry, spatial smoothing techniques have been considered on any arrays [6][7]. For this, the array of sensors is interpolated according to the goniometry adapted to spatial smoothing (or forward-backward). In [6] the interpolation technique addresses only a single angular segment and in [7] the algorithm is adapted to the cases of a number of angular segments for the interpolation. However, this kind of technique is difficult to adapt to the case of 2D goniometry.